Rpirls: quantitative predictions of rna interacting with any protein of known sequence
文献类型：期刊论文
| 作者 | Shen, Wen-Jun1; Cui, Wenjuan2; Chen, Danze1; Zhang, Jieming1; Xu, Jianzhen1 |
| 刊名 | Molecules
 |
| 出版日期 | 2018-03-01 |
| 卷号 | 23期号:3页码:14 |
| 关键词 | Protein-rna interactions Lncrna-protein interaction networks Derived kernel Regularized least squares |
| ISSN号 | 1420-3049 |
| DOI | 10.3390/molecules23030540 |
| 通讯作者 | Xu, jianzhen(jzxu01@stu.edu.cn) |
| 英文摘要 | Rna-protein interactions (rpis) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. as the number of available rna-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand rna-protein interactions by computational methods. in this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. the derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. we propose a novel machine learning method, called rpirls to predict the interaction between any rna and protein of known sequences. for the rpirls classifier, each protein sequence comprises up to 20 diverse amino acids but for the rpirls-7g classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. we evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, rpi-pred and ipminer. on the non-redundant benchmark test sets extracted from the pridb, the rpirls method outperformed rpi-pred and ipminer in terms of accuracy, specificity and sensitivity. further, rpirls achieved an accuracy of 92% on the prediction of incrna-protein interactions. the proposed method can also be extended to construct rna-protein interaction networks. the rpirls web server is freely available at http://bmc.med.stu.edu.cn/rpirls. |
| WOS关键词 | LONG NONCODING RNAS ; BINDING PROTEINS ; VIRUS-REPLICATION ; RIP-CHIP ; INSIGHTS ; SITES ; IDENTIFICATION ; INFORMATION ; EXPRESSION ; COMPONENTS |
| WOS研究方向 | Biochemistry & Molecular Biology ; Chemistry |
| WOS类目 | Biochemistry & Molecular Biology ; Chemistry, Multidisciplinary |
| 语种 | 英语 |
| WOS记录号 | WOS:000428514100031 |
| 出版者 | MDPI |
| URI标识 | http://www.irgrid.ac.cn/handle/1471x/2374233 |
| 专题 | 计算机网络信息中心 |
| 通讯作者 | Xu, Jianzhen |
| 作者单位 | 1.Shantou Univ, Med Coll, Dept Bioinformat, Shantou 515000, Guangdong, Peoples R China 2.Chinese Acad Sci, Comp Network Informat Ctr, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Shen, Wen-Jun,Cui, Wenjuan,Chen, Danze,et al. Rpirls: quantitative predictions of rna interacting with any protein of known sequence[J]. Molecules,2018,23(3):14. |
| APA | Shen, Wen-Jun,Cui, Wenjuan,Chen, Danze,Zhang, Jieming,&Xu, Jianzhen.(2018).Rpirls: quantitative predictions of rna interacting with any protein of known sequence.Molecules,23(3),14. |
| MLA | Shen, Wen-Jun,et al."Rpirls: quantitative predictions of rna interacting with any protein of known sequence".Molecules 23.3(2018):14. |
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来源：计算机网络信息中心
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